Narrow Results

Identifying influential nodes is of theoretical significance in many domains. Although lots of methods have been proposed to solve this problem, their evaluations are under single-source attack in scale-free networks. Meanwhile, some researches have speculated that the combinations of some methods may achieve more optimal results. In order to evaluate this speculation and design a universal strategy suitable for different types of networks under the consideration of multi-source attacks, this paper proposes an attribute fusion method with two independent strategies to reveal the correlation of existing ranking methods and indicators. One is based on feature union (FU) and the other is based on feature ranking (FR). Two different propagation models in the fields of recommendation system and network immunization are used to simulate the efficiency of our proposed method. Experimental results show that our method can enlarge information spreading and restrain virus propagation in the application of recommendation system and network immunization in different types of networks under the condition of multi-source attacks.

In this paper, we propose a new model for computer virus attacks and recovery at the level of information packets. The model we propose is based on one hand on the susceptible-infected (SI) and susceptible-infected-recovered (SIR) stochastic epidemic models for computer virus propagation and on the other hand on the time-discrete Markov chain of the minimal traffic routing protocol. We have applied this model to the scale free Barabasi–Albert network to determine how the dynamics of virus propagation is affected by the traffic flow in both the free-flow and the congested phases. The numerical results show essentially that the proportion of infected and recovered packets increases when the rate of infection $\lambda$ and the recovery rate $\beta$ increase in the free-flow phase while in the congested phase, the number of infected (recovered) packets presents a maximum (minimum) at certain critical value of $\beta$ characterizing a certain competition between the infection and the recovery rates.

This paper focuses on the internal law of online public opinion spreading under the conditions of different types of spreaders by constructing the model of public opinion spreading by subtypes of spreaders. The new $S{I}_S{I}_O{I}_N$ model is constructed by abstracting factors such as netizens’ interests, emotions and views on hot events in the Internet into propensity indices. The simulation results show that the spread of public opinion after categorizing netizens is more in line with the spread of public opinion in reality. Moreover, netizens tend to show different spreading behaviors in different public opinion cases, and the degree of change in netizens’ propensity varies greatly. All the data used in the simulation experiments are real data and analyzed with real events. The research results are helpful for what kind of control strategies are adopted after the occurrence of hotspot events.

This paper explores the law of emotion transmission on the social Internet. By applying higher-order network theory, ER random networks are reconstructed and higher-order network structures with multiple interaction points are generated. The I-state of the SI model is extended to set up the propagation rules of emotions based on higher-order networks. Concepts such as sentiment recession rate are introduced to make sentiment propagate through different simplexes, so as to obtain the propagation rules of higher-order networks. Qualitative analysis of emotional changes during the propagation process verifies the phase transition phenomenon of emotions in the network. By comparing simulated results with actual data, we demonstrate the accuracy of our higher-order network-based emotion propagation model in reflecting real-world trends and outcomes. The study also explores the effects of factors such as the initial proportion of extreme emotions on emotion propagation. This study provides valuable insights into understanding the operation of complex systems and offers important implications for government predictions of opinion development.

In this paper, we introduce the effect of neighbors on the infection of packets by computer virus in the SI and SIR models using the minimal traffic routing protocol. We have applied this model to the Barabasi–Albert network to determine how intrasite and extrasite infection rates affect virus propagation through the traffic flow of information packets in both the free-flow and the congested phases. The numerical results show that when we change the intrasite infection rate ${\lambda }_1$ while keeping constant the extrasite infection rate ${\lambda }_2$, we get normal behavior in the congested phase: in the network, the proportion of infected packets increases to reach a peak and then decreases resulting in a simultaneous increase of the recovered packets. In contrast, when the intrasite infection rate ${\lambda }_1$ is kept fixed, an increase of the extrasite infection rate results in two regimes: The first one is characterized by an increase of the proportion of infected packets until reaching some peak value and then decreases smoothly. The second regime is characterized by an increase of infected packets to some stationary value.

How to identify influential nodes in complex networks continues to be an open issue. A number of centrality measures have been presented to address this problem. However, these studies focus only on a centrality measure and each centrality measure has its own shortcomings and limitations. To solve the above problems, in this paper, a novel method is proposed to identify influential nodes based on combining of the existing centrality measures. Because information flow spreads in different ways in different networks, in the specific network, the appropriate centrality measures should be selected to calculate the ranking of nodes. Then, an interval can be generated for the ranking of each node, which includes the upper limit and lower limit obtained from different centrality measures. Next, the final ranking of each node can be determined based on the median of the interval. In order to illustrate the effectiveness of the proposed method, four experiments are conducted to identify vital nodes simulations on four real networks, and the superiority of the method can be demonstrated by the results of comparison experiments.

Spatial patterns are an important characteristic of ecological epidemic reaction–diffusion models, as they help predict the spatial dynamics of epidemic spread. However, there is currently a significant shortage in research on control strategies for generating and regulating these patterns. In this paper, we establish a Susceptible–Infectious (SI) model with Proportional-Derivative (PD) control and cross-diffusion. We analyze the stability of the model without diffusion and provide the conditions for Turing instability driven by diffusion. Using the cross-diffusion coefficient as the bifurcation parameter, we derive the amplitude equation of the two-dimensional Turing pattern at the Turing bifurcation threshold based on the multiple scale method, which determines the selection and stability of pattern formation. Numerical simulations in two-dimensional space show that the bifurcation parameter varies with changes in control parameters, and the PD control not only effectively alters the structure of Turing patterns but also suppresses Turing instability. Furthermore, it is found that more complex spatial patterns in three-dimensional space emerge under PD control.

We consider a two-patch SI model of integrated pest management with dispersal of both susceptible and infective pests between patches. A biological control, consisting of the periodic release of infective pests and a chemical control, consisting of periodic and impulsive pesticide spraying, are employed in order to maintain the size of the pest population below an economically acceptable level. It is assumed that the spread of the disease which is inflicted on the pest population through the use of the biological control is characterized by a nonlinear force of infection expressed in an abstract form. A sufficient condition for the local stability of the susceptible pest-eradication periodic solution is found using Floquet theory for periodic systems of ordinary differential equations, an analysis of the influence of dispersal between patches being performed for several particular cases. Our numerical simulations indicate that an increase in the amount but not in the frequency of pesticide use may not result in control. We also show that patches which are stable in isolation can be destabilized by dispersal between patches.

In the present paper, an epidemic model with Z-type control mechanism has been proposed and analyzed to explore the disease control strategy on an infectious disease outbreak. The uncontrolled model can have a disease-free equilibrium and an endemic equilibrium. The expression of the basic reproduction number and the conditions for the stability of the equilibria are derived. It is also observed that the disease-free equilibrium is globally asymptotically stable if $R_0<1$, whereas the endemic equilibrium is globally asymptotically stable if $R_0>1$. The model is further improved by considering Z-control mechanism and investigated. Disease can be controlled by using Z-control while the basic reproduction of the uncontrolled system is greater than unity. The positivity conditions of the solutions are derived and the basin of attraction for successful implementation of Z-control mechanism is also investigated. To verify the analytical findings, extensive numerical simulations on the model are carried out.